Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	143,249
2	Rhode Island	142,206
3	South Dakota	139,812
4	Utah	125,640
5	Tennessee	123,527
6	Arizona	119,999
7	Iowa	117,109
8	Wisconsin	115,075
9	Nebraska	115,028
10	South Carolina	114,385
11	Oklahoma	114,046
12	New Jersey	113,835
13	Arkansas	112,283
14	Alabama	110,186
15	Delaware	109,974
16	Indiana	109,819
17	Illinois	108,202
18	Kansas	107,827
19	New York	107,179
20	Florida	106,806
21	Idaho	106,517
22	Mississippi	105,850
23	Minnesota	105,522
24	Nevada	104,241
25	Montana	103,693
26	Georgia	102,801
27	Wyoming	102,325
28	Kentucky	101,941
29	Massachusetts	101,898
30	Texas	101,101
31	Louisiana	100,229
32	Missouri	99,273
33	Michigan	97,927
34	Connecticut	96,945
35	New Mexico	95,947
36	California	95,415
37	North Carolina	95,277
38	Alaska	94,364
39	Ohio	93,388
40	Pennsylvania	93,108
41	Colorado	92,967
42	West Virginia	88,521
43	Virginia	78,549
44	Maryland	75,493
45	New Hampshire	71,907
46	District of Columbia	68,848
47	Washington	55,861
48	Puerto Rico	53,410
49	Maine	49,035
50	Oregon	46,471
51	Vermont	38,321
52	Hawaii	24,187

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Alabama	166
2	South Carolina	152
3	Colorado	141
4	Maine	140
5	Michigan	134
6	Minnesota	133
7	West Virginia	133
8	Delaware	128
9	Florida	120
10	Indiana	119
11	Oregon	115
12	Pennsylvania	104
13	Illinois	97
14	Massachusetts	96
15	New York	86
16	New Mexico	85
17	North Carolina	85
18	New Hampshire	84
19	Rhode Island	82
20	Wyoming	82
21	Louisiana	79
22	Puerto Rico	79
23	District of Columbia	78
24	Utah	75
25	Washington	72
26	North Dakota	70
27	Idaho	69
28	Kentucky	68
29	Hawaii	66
30	Arizona	65
31	Ohio	64
32	Tennessee	64
33	Vermont	64
34	Alaska	61
35	Connecticut	61
36	Nevada	58
37	Wisconsin	57
38	Georgia	55
39	Maryland	52
40	Missouri	51
41	Montana	50
42	Virginia	48
43	New Jersey	47
44	Iowa	45
45	Texas	45
46	Kansas	41
47	Arkansas	40
48	Oklahoma	36
49	Mississippi	35
50	Nebraska	30
51	California	28
52	South Dakota	15

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,924
2	New York	2,696
3	Massachusetts	2,578
4	Rhode Island	2,549
5	Mississippi	2,438
6	Arizona	2,399
7	Connecticut	2,298
8	Louisiana	2,256
9	South Dakota	2,252
10	Alabama	2,251
11	Pennsylvania	2,100
12	North Dakota	2,012
13	Indiana	2,003
14	Michigan	1,982
15	New Mexico	1,963
16	Illinois	1,957
17	Arkansas	1,920
18	Iowa	1,903
19	South Carolina	1,875
20	Georgia	1,870
21	Nevada	1,796
22	Tennessee	1,790
23	Texas	1,763
24	Kansas	1,743
25	Oklahoma	1,738
26	Delaware	1,695
27	Florida	1,682
28	Ohio	1,670
29	District of Columbia	1,589
30	California	1,586
31	West Virginia	1,541
32	Missouri	1,530
33	Kentucky	1,529
34	Montana	1,495
35	Maryland	1,477
36	Wisconsin	1,324
37	Minnesota	1,310
38	Virginia	1,292
39	North Carolina	1,231
40	Wyoming	1,230
41	Nebraska	1,222
42	Idaho	1,158
43	Colorado	1,144
44	New Hampshire	980
45	Puerto Rico	763
46	Washington	746
47	Utah	704
48	Oregon	620
49	Maine	597
50	Alaska	474
51	Vermont	403
52	Hawaii	345

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	6
2	Alaska	5
3	Michigan	4
4	District of Columbia	2
5	Florida	2
6	Pennsylvania	2
7	Colorado	1
8	Connecticut	1
9	Illinois	1
10	Maryland	1
11	Massachusetts	1
12	New Jersey	1
13	New York	1
14	Oregon	1
15	Puerto Rico	1
16	South Carolina	1
17	Virginia	1
18	West Virginia	1
19	Alabama	0
20	Arizona	0
21	Arkansas	0
22	California	0
23	Delaware	0
24	Georgia	0
25	Hawaii	0
26	Idaho	0
27	Indiana	0
28	Iowa	0
29	Kansas	0
30	Louisiana	0
31	Maine	0
32	Minnesota	0
33	Mississippi	0
34	Missouri	0
35	Montana	0
36	Nevada	0
37	New Hampshire	0
38	New Mexico	0
39	North Carolina	0
40	North Dakota	0
41	Ohio	0
42	Oklahoma	0
43	Rhode Island	0
44	South Dakota	0
45	Tennessee	0
46	Texas	0
47	Utah	0
48	Vermont	0
49	Washington	0
50	Wisconsin	0
51	Wyoming	0
52	Nebraska	-2

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	375,447	1	99
Crowley	Colorado	362,646	2	99
Bent	Colorado	275,417	3	99
Dewey	South Dakota	250,170	4	99
Lincoln	Arkansas	246,007	5	99
Davidson	Tennessee	142,508	189	93
Richland	South Carolina	112,698	981	68
York	South Carolina	111,887	1013	67
Orange	California	85,429	2233	28
Pierce	Washington	58,294	2850	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,804	5	99
Orange	California	1,584	1820	42
York	South Carolina	1,363	2078	33
Davidson	Tennessee	1,357	2089	33
Richland	South Carolina	1,349	2103	33
Pierce	Washington	737	2739	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.